Advanced Detection Techniques Research Articles

Unveiling the dynamics of circulating tumor cells in colorectal cancer: from biology to clinical applications

This review delves into the pivotal role of circulating tumor cells (CTCs) in colorectal cancer (CRC) metastasis, focusing on their biological properties, interactions with the immune system, advanced detection techniques, and clinical implications. We explored how metastasis-competent CTCs evade immune surveillance and proliferate, utilizing cutting-edge detection and isolation technologies, such as microfluidic devices and immunological assays, to enhance sensitivity and specificity. The review highlights the significant impact of CTC interactions with immune cells on tumor progression and patient outcomes. It discusses the application of these findings in clinical settings, including non-invasive liquid biopsies for early diagnosis, prognosis, and treatment monitoring. Despite advancements, challenges remain, such as the need for standardized methods to consistently capture and analyze CTCs. Addressing these challenges through further molecular and cellular research on CTCs could lead to improved interventions and outcomes for CRC patients, underscoring the importance of unraveling the complex dynamics of CTCs in cancer progression.

Enhancing financial fraud detection using adaptive machine learning models and business analytics

Financial fraud continues to be a critical threat to businesses and economies worldwide, necessitating advanced detection techniques. This paper reviews the role of adaptive machine learning (ML) models and business analytics in enhancing fraud detection systems. Traditional fraud detection methods often fall short in addressing the complexity and evolving nature of fraudulent activities, making adaptive ML models, such as decision trees and neural networks, more effective in identifying subtle patterns in large datasets. Organizations can refine ML models by integrating business analytics, ensuring real-time detection and continuous improvement of fraud detection systems. The paper also explores the challenges in deploying these technologies, including data privacy concerns and model accuracy, as well as the potential impact of emerging technologies such as blockchain and quantum computing. Future directions emphasize improving the interpretability of ML models and leveraging multi-modal data for a more holistic approach to fraud prevention. The synergy between adaptive machine learning and business analytics promises a more resilient and effective framework for combating financial fraud.

Advanced Detection Techniques to neutralize Supply Chain Cyber-attacks on small and medium businesses in the United States: The Machine Learning and AI Approach.

Cyber-attacks targeting supply chains represent a significant and increasing risk for small and medium-sized businesses (SMBs) in the United States. This paper examines the role of machine learning (ML) and artificial intelligence (AI) in detecting and mitigating these cybersecurity threats. Key challenges faced by SMBs include high implementation costs, a shortage of skilled personnel, inadequate data quality, and the complexity of integrating advanced technologies into existing systems. Despite these hurdles, the paper identifies strategic approaches that can empower SMBs to effectively leverage ML and AI, including affordable solutions, targeted training, and improved data management practices. It emphasizes the importance of addressing privacy and usability concerns to facilitate successful technology adoption. Recommendations include pursuing cost-effective, subscription-based AI and ML models, utilizing government incentives, and enhancing workforce skills through training and partnerships. Additionally, collaboration with larger organizations can improve data management and system efficacy. By implementing robust protective measures and increasing awareness through educational initiatives, SMBs can strengthen their cybersecurity posture and better navigate the evolving landscape of cyber threats.

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

“Idol talks!” AI-driven image to text to speech: illustrated by an application to images of deities

This work aims to provide an innovative solution to enhance the accessibility of images by an innovative image to text to speech system. It is applied to Hindu and Christian divine images. The method is applicable, among others, to enhance cultural understanding of these images by the visually impaired. The proposed system utilizes advanced object detection techniques like YOLO V5 and caption generation techniques like ensemble models. The system accurately identifies significant objects in images of Deities. These objects are then translated into descriptive and culturally relevant text through a Google text-to-speech synthesis module. The incorporation of text generation techniques from images introduces a new perspective to the proposed work. The aim is to provide a more comprehensive understanding of the visual content and allow visually impaired individuals to connect with the spiritual elements of deities through the immersive experience of auditory perception through a multimodal approach to make them feel inclusive in the community. This work is also applicable to preserve Cultural Heritage, Tourism and integrating with Virtual Reality (VR) and Augmented Reality (AR). Images of the artistic cultural legacy are hardly available in annotated databases, particularly those featuring idols. So we gathered, transcribed, and created a new database of Religious Idols in order to satisfy this requirement. In this paper, we experimented how to handle an issue of religious idol recognition using deep neural networks. In order to achieve this outcome, the network is first pre-trained on various deep learning models, and the best one which outperforms others is chosen. The proposed model achieves an accuracy of 96.75% for idol detection, and an approximate 97.06% accuracy for text generation according to the BLEU score.

Deep learning-based proactive fault detection method for enhanced quadrotor safety

The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.

Recent Advances in Monitoring Microbial Toxins in Food Samples by HPLC-Based Techniques: A Review

This study examines the significant impact of bacterial, algal, and fungal toxins on foodborne illnesses, and stresses the importance of advanced detection techniques, such as high-performance liquid chromatography (HPLC)-based methodologies. It emphasizes the urgent need for further advancements in these techniques to ensure food safety, as they offer significant benefits, including low detection limits and the ability to be combined with other techniques to detect a wide range of toxins. In this regard, HPLC has emerged as a versatile and sensitive analytical technique for this purpose. Various HPLC methods, often enhanced with detectors such as ultraviolet (UV), fluorescence (FD), and mass spectrometry (MS), have been developed to identify and quantify microbial toxins in a wide variety of food samples. Recent advancements include HPLC-FD methods that utilize the natural fluorescence of certain aflatoxins, improving detection sensitivity. HPLC-MS/MS and UHPLC-MS/MS techniques offer high selectivity and sensitivity, making them suitable for detecting a wide range of toxins in trace quantities. The adaptability of HPLC, combined with innovative detection technologies and sample preparation methods, holds significant potential for enhancing food safety monitoring and reducing the global burden of foodborne diseases.

Secondary vertex reconstruction with MaskFormers

In high-energy particle collisions, the reconstruction of secondary vertices from heavy-flavour hadron decays is crucial for identifying and studying jets initiated by b- or c-quarks. Traditional methods, while effective, require extensive manual optimisation and struggle to perform consistently across wide regions of phase space. Meanwhile, recent advancements in machine learning have improved performance but are unable to fully reconstruct multiple vertices. In this work we propose a novel approach to secondary vertex reconstruction based on recent advancements in object detection and computer vision. Our method directly predicts the presence and properties of an arbitrary number of vertices in a single model. This approach overcomes the limitations of existing techniques. Applied to simulated proton-proton collision events, our approach demonstrates significant improvements in vertex finding efficiency, achieving a 10% improvement over an existing state-of-the-art method. Moreover, it enables vertex fitting, providing accurate estimates of key vertex properties such as transverse momentum, radial flight distance, and angular displacement from the jet axis. When integrated into a flavour tagging pipeline, our method yields a 50% improvement in light-jet rejection and a 15% improvement in c-jet rejection at a b-jet selection efficiency of 70%. These results demonstrate the potential of adapting advanced object detection techniques for particle physics, and pave the way for more powerful and flexible reconstruction tools in high-energy physics experiments.

Advanced Fraud Threats And Detection In Browser, Android, And Ios Environments

As digital technologies evolve, so do the methods employed by cybercriminals to exploit vulnerabilities within browser and mobile environments. This paper explores advanced fraud threats such as VPN detection evasion, browser spoofing, mobile device factory resets, jailbroken phones, user-initiated app cloning, and Android emulators and tampering. We discuss how these threats are misused and outline advanced detection techniques to mitigate risks. The paper emphasizes the importance of implementing robust security measures to protect against sophisticated fraud tactics in browser, Android, and iOS platforms.

Advanced Fraud Threats And Detection In Browser, Android, And Ios Environments

As digital technologies evolve, so do the methods employed by cybercriminals to exploit vulnerabilities within browser and mobile environments. This paper explores advanced fraud threats such as VPN detection evasion, browser spoofing, mobile device factory resets, jailbroken phones, user-initiated app cloning, and Android emulators and tampering. We discuss how these threats are misused and outline advanced detection techniques to mitigate risks. The paper emphasizes the importance of implementing robust security measures to protect against sophisticated fraud tactics in browser, Android, and iOS platforms.

Advanced Fraud Threats and Detection in Browser, Android, and iOS Environments

As digital technologies evolve, so do the methods employed by cybercriminals to exploit vulnerabilities within browser and mobile environments. This paper explores advanced fraud threats such as VPN detection evasion, browser spoofing, mobile device factory resets, jailbroken phones, user-initiated app cloning, and Android emulators and tampering. We discuss how these threats are misused and outline advanced detection techniques to mitigate risks. The paper emphasizes the importance of implementing robust security measures to protect against sophisticated fraud tactics in browser, Android, and iOS platforms.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Secondary Glaucoma Following Corneal Transplantation: A Comprehensive Review of Pathophysiology and Therapeutic Approaches.

Corneal transplantation is a critical surgical procedure aimed at restoring vision in patients with corneal blindness or severe damage. This review focuses on secondary glaucoma, a significant postoperative complication, with the primary objective of providing a comprehensive analysis of its pathophysiology, risk factors, diagnostic challenges, and therapeutic approaches. Unlike other reviews, this work emphasizes the interplay between inflammatory responses, corticosteroid use, and structural changes in the eye that lead to elevated intraocular pressure (IOP) after transplantation. A comprehensive review of the literature was conducted, including studies on postcorneal transplantation glaucoma, to highlight both clinical outcomes and the efficacy of current management strategies. Key findings indicate that medical treatments, such as prostaglandin analogs and beta-blockers, are effective for IOP control in the early stages, while surgical interventions, like trabeculectomy, are often necessary for more advanced cases. Diagnostic challenges, such as the difficulty of accurate IOP measurement posttransplant, are underscored, along with the importance of advanced imaging techniques for the early detection of optic nerve damage. The pathophysiology of secondary glaucoma involves a complex interaction of postsurgical inflammation, steroid-induced complications, and anatomical changes that hinder aqueous humor outflow. Diagnosis requires a combination of tonometry, gonioscopy, and imaging technologies. Management strategies range from pharmacological treatments to surgical options, with a critical focus on balancing IOP control and minimizing risks to graft survival. Clinically, these findings highlight the need for proactive and tailored management of IOP in corneal transplant patients to preserve both graft function and long-term visual outcomes. Future research should focus on improving diagnostic accuracy, developing less invasive surgical techniques, and exploring personalized medicine approaches, including genetic profiling and targeted therapies.

Assessing Tsunami Impacts and Enhancing Disaster Response in Tirtayasa Banten through High-Resolution Satellite Imagery

In December 2018, the coastal town of Tirtayasa, Banten, suffered severe damage from a tsunami, leaving the area highly vulnerable to future disasters. This research assesses the tsunami’s impact, thoroughly evaluating damage to infrastructure and vegetation using high-resolution satellite imagery. By comparing pre- and post-tsunami images, we quantitatively measure resilience and devastation, documenting significant landscape changes to better understand the extent of damage and identify resilient areas. These insights are critical for developing effective disaster response plans. The study employs advanced geospatial analytic techniques, demonstrating how satellite imagery enhances disaster preparedness and management by enabling prompt and accurate assessments, which are essential for both emergency response and long-term recovery planning. Integrating satellite-based remote monitoring into standard disaster management practices offers substantial advantages, improving the preparedness and response capabilities of vulnerable areas. This research highlights the importance of advanced change detection techniques to improve the accuracy of impact assessments and foster the development of targeted measures to mitigate the effects of future natural disasters.

Electrocatalytic efficiency of carbon nitride supported gold nanoparticle based sensor for iodide and cysteine detection

Extensive investigations are being conducted on gold nanoparticles focusing on their applications in biosensors, laser phototherapy, targeted drug delivery and bioimaging utilizing advanced detection techniques. In this work, an electrochemical sensor was developed based on graphite carbon nitride supported gold nanoparticles. Carbon nitride supported gold nanoparticles (Au–CN) was synthesized by applying a deposition-precipitation route followed by a chemical reduction technique. The composite system was characterized by X-ray diffraction and X-ray photo electron spectroscopy methods. Electron microscopy analysis confirmed the formation of gold nanoparticles within the size range of 5–15 nm on the carbon nitride support. Carbon nitride supported gold based sensor was employed for the electrochemical detection of iodide ion and l-cysteine. The limit of detection and sensitivity of the sensor was attained 8.9 μM and 0.96 μAμM⁻1cm⁻2, respectively, for iodide ion, while 0.48 μM and 5.8 μAμM⁻1cm⁻2, respectively, was achieved for the recognition of cysteine. Furthermore, a paper-based electrochemical device was developed using the Au–CN hybrid system that exhibited promising results in detecting iodide ions, highlighting its potential for economic and portable device applications.

Microsaccades Tracking by Secondary Speckle Pattern Analysis.

Here we propose a not pupil-dependent microsaccades tracking technique and a novel detection method. We present a proof of concept for detecting microsaccades using a non-contact laser-based photonic system recording and processing the temporal changes of speckle patterns scattered from an eye sclera. The data, simultaneously recorded by the speckle-based tracker (SBT) and the video-based eye tracker (Eyelink), was analyzed by the frequently used detection method of Engbert and Kliegl (E&K) and by advanced machine learning detection (MLD) techniques. We detected 93% of microsaccades in the SBT data out of microsaccades detected in the Eyelink data with the E&K method. By utilizing MLD, a precision of 86% was achieved. The findings of our study demonstrate a potential improvement in measuring tiny eye movements, such as microsaccades, using speckle-based eye tracking and, thus, an alternative to video-based eye tracking for detecting microsaccades.

Conventional and advanced techniques for detection of seed borne pathogens

Conventional and advanced techniques for detection of seed borne pathogens

Enhancing crack detection in railway tracks through AI-optimized ultrasonic guided wave modes

The utilization of ultrasonic guided wave technology for detecting cracks in railway tracks involves analyzing echo signals produced by the interaction of cracks with guided wave modes to achieve precise crack localization, which is extremely important in a real-time railway crack robotic detection system. Addressing the challenge of selecting the optimal detection mode for cracks in various regions of railway tracks, this paper presents a method for optimal crack detection mode selection. This method is based on the sensitivity of guided wave modes to cracks. By examining the frequency dispersion characteristics and mode shapes of guided wave modes, we establish indicators for crack zone energy and crack reflection intensity. Our focus is on the railhead of the railway track, selecting guided wave modes characterized by specific cracks for detection purposes. Experimental findings validate the accuracy of our proposed mode selection method in detecting cracks in railway tracks. This research not only enhances crack detection but also lays the groundwork for exploring advanced detection and localization techniques for cracks in railway tracks.

Recent advances in detection techniques for RNA modifications

Recent advances in detection techniques for RNA modifications

Wearable Kapton graphene biosensor for detection of toxic gases

Toxic gases cause a significant number of human deaths every year across the globe. Different types of harmful substances can affect individuals in various ways. Inhaling these gases can have harmful consequences for the body, leading to physiological changes or even death. As a preliminary measure, a simulation application is utilized to detect certain gases by utilizing small-scale and flexible architectures, along with advanced detection techniques. The active material used in this study is a patch of emitting graphene embedded on a polyimide film (Kapton), which helps to determine the frequency of the RF Planar Resonant Structure. We plan to utilize the graphene-Kapton sensor for conducting non-invasive testing. The device has been tested for detecting and recognizing various dangerous and toxic gases, including Fluorine azide (F2N), Hydrogen Iodide (HI), Nitrogen (N2), Methane (CH4), and Carbon monoxide (CO). The simulation results suggest that the Graphene-Kapton flexible sensor provides excellent detection performance. The sensor can accurately identify each gas based on its distinct characteristics. Furthermore, ample sensitivity analysis against geometrical parameters and external electrical stimulation is performed to show the robustness and reliability of the proposed structure.